Privacy

Empirically evaluating privacy in machine learning III: functional & gaussian differential privacy

The third in a series of blog posts on machine learning and privacy: why functional differential privacy is useful for auditing privacy in machine learning.

Empirically evaluating privacy in machine learning II: Hypothesis testing

The second in a series of blog posts on machine learning and privacy: Using statistics to think like a hacker, so we can avoid them!

Empirically evaluating privacy in machine learning I: Introduction

The first in a series of blog posts on machine learning and privacy.

Empirical Privacy Evaluations of Generative and Predictive Machine Learning Models -- A review and challenges for practice

Synthetic data generators, when trained using privacy-preserving techniques like differential privacy, promise to produce synthetic data with formal privacy guarantees, facilitating the sharing of sensitive data. However, it is crucial to empirically assess …